CN113114935A - Vibration identification method based on video image - Google Patents
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Abstract
The invention relates to the technical field of vibration identification, in particular to a vibration identification method based on a video image, which comprises the following steps: s1: collecting environmental data, wherein the environmental data comprises rainfall; s2: controlling the shooting frequency according to the acquired environmental data, and continuously shooting the object to be detected according to the shooting frequency; when the rainfall reaches a rainfall threshold value, the shooting frequency is accelerated; s3: preprocessing the shot image; s4: and acquiring a pixel skeleton line segment of the edge of the object in the preprocessed image, acquiring a target point on the pixel skeleton line segment in the continuously shot image in the same way, calculating vibration data of the target point, and generating slope displacement variation. The method can realize vibration identification through edge information of the side slope based on a video image processing technology, and can control the detection frequency according to environmental factors causing side slope displacement, and accelerate the detection frequency when the side slope displacement is easy to occur.
Description
Technical Field
The invention relates to the technical field of vibration identification, in particular to a vibration identification method based on a video image.
Background
Since the twentieth century, a high-speed camera shooting technology and a data transmission technology are gradually developed, a video image processing technology is rapidly grown, the change of a measurement technology is greatly promoted, at present, the video image processing technology is widely applied to aspects such as national defense and military industry, aerospace, robot vision, medical bioengineering, industrial product detection and the like, and the video image processing technology has important application value in vibration identification and detection.
Due to the restriction of geological conditions and the restriction of highway linearity in China, the problems of collapse, landslide, slope displacement caused by slope instability and the like become considerable potential safety hazards. In order to accurately monitor the displacement of the side slope and forecast the development trend of deformation, the prior art mostly adopts a mode of laying and pasting marks or textures on the surface of the side slope to realize dynamic tracking of characteristic points. However, such a method has a problem that it is difficult to lay and attach a mark to the slope vibration detection located outdoors, and the mark is likely to fall off. Therefore, based on the video image processing technology, the vibration recognition is realized through the edge information of the detected slope, and the method becomes a breakthrough point in the slope vibration recognition field. How to control the detection frequency according to the environmental factors causing the slope displacement also becomes a problem to be solved urgently.
Disclosure of Invention
The invention provides a vibration identification method based on a video image, which can realize vibration identification through edge information of a side slope based on a video image processing technology, and can control the detection frequency according to environmental factors causing side slope displacement, and accelerate the detection frequency when the side slope displacement is easy to occur.
The basic scheme provided by the invention is as follows:
a vibration identification method based on video images comprises the following steps:
s1: collecting environmental data, wherein the environmental data comprises rainfall;
s2: controlling the shooting frequency according to the acquired environmental data, and continuously shooting the object to be detected according to the shooting frequency; when the rainfall reaches a rainfall threshold value, the shooting frequency is accelerated;
s3: preprocessing the shot image;
s4: and acquiring a pixel skeleton line segment of the edge of the object in the preprocessed image, acquiring a target point on the pixel skeleton line segment in the continuously shot image in the same way, calculating vibration data of the target point, and generating slope displacement variation.
The principle and the advantages of the invention are as follows: the displacement of the side slope is related to surrounding environmental factors, environmental data are collected, the shooting frequency is controlled according to the collected environmental data, if the rainfall reaches a rainfall threshold value, the shooting frequency is accelerated, and therefore the effect of accelerating the detection frequency when the displacement of the side slope is easy to occur is achieved; the method comprises the steps of obtaining a pixel skeleton line segment of an object edge in a shot image, and generating a slope displacement variable quantity according to the pixel skeleton line segment of the object edge in the image.
Further, the environment data further comprises wind power and a wind direction, the wind power reaches a wind power threshold, and when an included angle between the wind direction and the slope surface of the side slope is smaller than an angle threshold, the shooting frequency is accelerated.
Has the advantages that: after the plant on the side slope receives the effect of wind, transmit its load for the side slope, wind-force is big more, and the domatic contained angle of wind direction and side slope is little more, and the plant on the side slope receives the power of wind just big more to the load of transmitting for the side slope just is big more, can derive, and the displacement of side slope can receive the influence of wind-force and wind direction, and wind-force reaches the wind-force threshold value, and when the wind direction was less than the angle threshold value with the domatic contained angle of side slope, the easy side slope displacement that takes place, so accelerate the frequency of.
Further, the environment data comprises the weight of vehicles passing through the road, and when the weight of the vehicles reaches a weight threshold value, the objects to be measured are continuously shot.
Has the advantages that: the external factors influencing the displacement of the side slope comprise vibration, the larger the weight of a vehicle passing through the road is, the larger the vibration is when the vehicle passes through the road near the side slope, and therefore the influence on the displacement of the side slope is larger.
Further, the S3 includes:
s301: carrying out noise reduction processing on the shot image;
s302: and performing threshold segmentation on the image subjected to the noise reduction processing to obtain a binary image.
Has the advantages that: and performing noise reduction and threshold segmentation on the image to obtain a binary image, which is favorable for obtaining a pixel skeleton line segment of a slope edge in the image.
Further, a manner of performing noise reduction processing on the captured image is median filtering.
Has the advantages that: the median filtering adopts a nonlinear method, is very effective in smoothing impulse noise, can protect sharp edges of an image, and selects proper points to replace values of pollution points, so that the processing effect is good, and the impulse noise is better represented.
Further, adopt high-speed camera to the shooting of the object that awaits measuring, respectively set up a high-speed camera in the left and right sides of side slope at least, still include S5: and taking the average value of the slope displacement variation obtained by the images shot by the two high-speed cameras as the adjusted slope displacement variation.
Has the advantages that: and a plurality of high-speed cameras are adopted to shoot images and calculate the slope displacement variation, so that the accuracy of the calculation result is improved.
Further, in S4, multiple target points of the object to be detected may be acquired simultaneously, and the above-mentioned identification method may calculate vibration data of the multiple target points simultaneously.
Has the advantages that: the multiple target points are acquired simultaneously, the working efficiency is improved, and more comprehensive slope displacement variable quantity can be reflected by simultaneously calculating the vibration data of the multiple target points.
Further, the method also comprises the step of S6: and when the adjusted slope displacement variation reaches a variation threshold, giving a prompt.
Has the advantages that: and when the slope displacement variation reaches a variation threshold, prompting surrounding personnel.
Drawings
Fig. 1 is a logic block diagram of a vibration identification method based on a video image in embodiment 1 of the present invention.
Fig. 2 is a logic block diagram of a vibration identification method based on a video image in embodiment 2 of the present invention.
Detailed Description
The following is further detailed by way of specific embodiments:
example 1 is substantially as shown in figure 1:
a vibration identification method based on video images comprises the following steps:
s1: environmental data is collected.
S2: according to the method, the shooting frequency is controlled according to the acquired environmental data, and the object to be detected is continuously shot according to the shooting frequency.
S3: preprocessing the shot image; s301: performing noise reduction processing on the shot image, wherein in the embodiment, the mode of performing noise reduction processing on the shot image is median filtering; s302: and performing threshold segmentation on the image subjected to noise reduction processing to obtain a binary image, wherein in the embodiment, the method for selecting the image binarization threshold is a maximum inter-class variance method.
S4: and acquiring a pixel skeleton line segment of the edge of the object in the preprocessed image, acquiring a target point on the pixel skeleton line segment in the continuously shot image in the same way, calculating vibration data of the target point, and generating slope displacement variation. The method can be used for simultaneously acquiring a plurality of target points of the object to be detected, and the identification method can be used for simultaneously calculating the vibration data of the target points.
S5: and taking the average value of the slope displacement variation obtained by the images shot by the two high-speed cameras as the adjusted slope displacement variation.
S6: and when the adjusted slope displacement variation reaches a variation threshold, giving a prompt. In this embodiment, the variation threshold is 3 cm; in this embodiment, a buzzer is used for prompting.
Environmental data includes rainfall, wind direction and weight of vehicles passing in the road. In this embodiment, adopt rainfall sensor to detect the rainfall near the side slope, when the rainfall reached the rainfall threshold, accelerate the shooting frequency, in this embodiment, the rainfall threshold is that 10min rainfall intensity is greater than or equal to 4mm, when the rainfall that detects reaches this rainfall threshold, carry out the shooting frequency from every thirty minutes, accelerate to carry out the shooting once every ten minutes, when the rainfall that detects is less than this rainfall threshold, reset the shooting frequency and carry out the shooting once every thirty minutes.
After the plants on the side slope receive the effect of wind, transmit its load to the side slope, wind-force is big more, and the domatic contained angle of wind direction and side slope is little more, and the plant on the side slope receives the power of wind just more big to the load that transmits the side slope just is big more, can draw, and the displacement of side slope can receive the influence of wind-force and wind direction. Therefore, adopt wind sensor to detect the wind-force near the side slope in this embodiment, adopt wind direction sensor to detect the wind direction near the side slope, when the wind-force that detects reaches the wind threshold value, and the wind direction is less than the angle threshold value with the domatic contained angle of side slope for shoot the frequency and carry out once every ten minutes. In this embodiment, the wind threshold is 9 levels, the angle threshold is 45 degrees, and when the detected wind power is smaller than the wind threshold or the included angle between the wind direction and the slope surface of the side slope is larger than the angle threshold, the shooting frequency is reset to once every thirty minutes to perform shooting.
The external factors influencing the displacement of the side slope comprise vibration, the larger the weight of a vehicle passing through the road is, the larger the vibration is when the vehicle passes through the road near the side slope, and therefore the influence on the displacement of the side slope is larger. In this example, the weight threshold is 12 tons.
Preprocessing the image shot each time, wherein the shot image has noise, so that the image is smoother and the edge is clearer by noise reduction treatment; in order to segment the background and the side slope in the image, threshold segmentation is carried out on the image after noise reduction processing, and a binary image is obtained.
And performing rough positioning on the edge of the object on the preprocessed image to obtain a pixel skeleton line segment of the edge of the object. In this embodiment, the target point is obtained by equally dividing the pixel skeleton line segment into 4 parts, and taking three points of the divided pixel skeleton line segment as the target points, which are respectively a target point 1, a target point 2, and a target point 3.
The same target point acquired in the same manner in the continuously captured images is subjected to dynamic position tracking, such as the target point 1 in the first captured image and the target point 1 in the second captured image. In the scheme, two high-speed cameras are adopted for shooting simultaneously, so that the images shot by the two high-speed cameras simultaneously can be matched with the same target point, for example, the target point 2 in the image shot by the high-speed camera 1 and the target point 2 in the image shot by the high-speed camera 2 are matched at the same time. And calculating vibration data of the target point to generate slope displacement variation.
Example 2 is substantially as shown in figure 2:
embodiment 2 is the same in basic principle as embodiment 1, except that embodiment 2 further includes S7: and acquiring weather forecast information, and estimating the risk coefficient of the slope landslide at each time point according to the rainfall, the wind power and the wind direction of the slope location at each time point in the weather forecast.
Specifically, the risk coefficient of the slope landslide at each time point is estimated through an artificial intelligence algorithm, rainfall, wind direction and wind power are used as input of an input layer, and the risk coefficient of the slope landslide is used as output of an output layer. Firstly, a three-layer BP neural network model is constructed, wherein the model comprises an input layer, a hidden layer and an output layer, in the embodiment, rainfall, wind direction and wind power are used as input of the input layer, so that the input layer has 3 nodes, and the output is the risk coefficient of landslide of a side slope, so that 1 node is provided in total, and in the embodiment, the risk coefficient of landslide of the side slope is 0-1; for hidden layers, the present embodiment uses the following formula to determine the number of hidden layer nodes:
where l is the number of nodes of the hidden layer, n is the number of nodes of the input layer, m is the number of nodes of the output layer, and a is a number between 1 and 10, which is taken as 6 in this embodiment, so that the hidden layer has 8 nodes in total. BP neural networks typically employ Sigmoid differentiable functions and linear functions as the excitation function of the network. This example selects the S-type tangent function tansig as the excitation function for hidden layer neurons. The prediction model selects an S-shaped logarithmic function tansig as an excitation function of neurons of an output layer. After the BP network model is built, the node data in the historical data are used as samples to train the model, and a more accurate prediction result can be obtained through the prediction model obtained after the node data training is completed.
S8: the method comprises the steps of obtaining a license plate number of a vehicle with the residence time under the side slope exceeding a time threshold value, obtaining identity information of a vehicle owner according to the license plate number, wherein the identity information comprises a mobile phone number, obtaining identity information of relatives and friends of the vehicle owner, obtaining navigation information and positioning information in navigation software logged in through the mobile phone number of the vehicle owner or relatives and friends of the vehicle owner according to the identity information of the vehicle owner, and the navigation information comprises a destination, a distance traveled in single navigation and time traveled in single navigation. In this embodiment, the time threshold is 5 minutes, and in this embodiment, the identity information of the vehicle owner and the relatives and friends thereof is obtained through big data.
S9: and acquiring the positioning information as navigation information in a terminal where the side slope is located.
S10: and predicting the stay time of the vehicle according to the acquired navigation information in the terminal.
Specifically, the stopping time of the vehicle is predicted through an artificial intelligence algorithm, the destination, the distance traveled in single navigation and the time traveled in single navigation are used as input of an input layer, and the stopping time of the vehicle is used as output of an output layer. Firstly, a three-layer BP neural network model is constructed, wherein the model comprises an input layer, a hidden layer and an output layer, in the embodiment, a destination, a distance traveled in single navigation and time traveled in single navigation are used as the input of the input layer, so that the input layer has 3 nodes, and the output is the staying time of a vehicle, so that 1 node is total; for hidden layers, the present embodiment uses the following formula to determine the number of hidden layer nodes:
where l is the number of nodes of the hidden layer, n is the number of nodes of the input layer, m is the number of nodes of the output layer, and a is a number between 1 and 10, which is taken as 6 in this embodiment, so that the hidden layer has 8 nodes in total. BP neural networks typically employ Sigmoid differentiable functions and linear functions as the excitation function of the network. This example selects the S-type tangent function tansig as the excitation function for hidden layer neurons. The prediction model selects an S-shaped logarithmic function tansig as an excitation function of neurons of an output layer. After BP network model constructionAnd then, training the model by using the node data in the historical data as a sample, and obtaining a more accurate prediction result through the prediction model obtained after the node data training is finished.
S11: and setting a time period between the current time point and the time point after the superposition of the predicted stay time as a stay time period, and sending a prompt short message to the mobile phone number of the vehicle owner or the relatives and friends of the vehicle when the risk coefficient of the slope landslide is greater than the coefficient threshold value between the stay time periods. In this embodiment, the coefficient threshold is 0.5.
For example: the current time point is 12:00, the predicted retention time is 3 hours, so the retention time period is 12:00-15:00, and if the risk coefficient of the slope landslide at any time point is more than 0.5 during the retention time period, a prompt short message is sent to the mobile phone number of the vehicle owner or the relatives and friends of the vehicle owner.
By adopting the scheme, the risk coefficient of landslide at each time point of the side slope can be predicted according to the weather condition, and the risk coefficient of landslide in the time period when the vehicle stops near the side slope is predicted by predicting the stop time of the vehicle stopping near the side slope, so that the vehicle is reminded.
Firstly, the traditional vibration identification can only detect when the side slope generates vibration displacement, and when the side slope is detected to have vibration displacement, vehicles parked nearby cannot be evacuated in time, the risk coefficient of the side slope generating landslide is estimated, the time of the vehicle staying is estimated, the risk coefficient of the landslide in the time period of the vehicle staying is analyzed, and when danger possibly exists, the vehicle owner and relatives and friends are prompted, so that the effect of avoiding the risk is achieved. Secondly, the scheme estimates the residence time of each vehicle, and can meet the parking requirement of the vehicle without landslide risk near the side slope in other residence time periods.
The foregoing are merely exemplary embodiments of the present invention, and no attempt is made to show structural details of the invention in more detail than is necessary for the fundamental understanding of the art, the description taken with the drawings making apparent to those skilled in the art how the several forms of the invention may be embodied in practice with the teachings of the invention. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (8)
1. A vibration identification method based on video images is characterized in that: the method comprises the following steps:
s1: collecting environmental data, wherein the environmental data comprises rainfall;
s2: controlling the shooting frequency according to the acquired environmental data, and continuously shooting the object to be detected according to the shooting frequency; when the rainfall reaches a rainfall threshold value, the shooting frequency is accelerated;
s3: preprocessing the shot image;
s4: and acquiring a pixel skeleton line segment of the edge of the object in the preprocessed image, acquiring a target point on the pixel skeleton line segment in the continuously shot image in the same way, calculating vibration data of the target point, and generating slope displacement variation.
2. The video-image-based vibration recognition method according to claim 1, characterized in that: the environment data further comprises wind power and wind direction, and when the wind power reaches a wind power threshold value and an included angle between the wind direction and the slope surface of the side slope is smaller than an angle threshold value, shooting frequency is accelerated.
3. The video-image-based vibration recognition method according to claim 1, characterized in that: the environmental data comprises the weight of vehicles passing through the road, and when the weight of the vehicles reaches a weight threshold value, the objects to be measured are continuously shot.
4. The video-image-based vibration recognition method according to claim 1, characterized in that: the S3 includes:
s301: carrying out noise reduction processing on the shot image;
s302: and performing threshold segmentation on the image subjected to the noise reduction processing to obtain a binary image.
5. The video-image-based vibration recognition method according to claim 4, wherein: the mode of performing noise reduction processing on the captured image is median filtering.
6. The video-image-based vibration recognition method according to claim 1, characterized in that: adopt high-speed camera to the shooting of awaited measuring object, respectively set up a high-speed camera in the left and right sides of side slope at least, still include S5: and taking the average value of the slope displacement variation obtained by the images shot by the two high-speed cameras as the adjusted slope displacement variation.
7. The video-image-based vibration recognition method according to claim 1, characterized in that: in S4, multiple target points of the object to be detected may be acquired simultaneously, and the above-mentioned identification method may calculate vibration data of multiple target points simultaneously.
8. The video-image-based vibration recognition method according to claim 6, wherein: further comprising S6: and when the adjusted slope displacement variation reaches a variation threshold, giving a prompt.
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